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Innovation Brief Portfolio Selection of the NASDAQ and NYSE Energy Sectors using Genetic Optimization<br />By Evan, L, Hje...
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  1. 1. Innovation Brief Portfolio Selection of the NASDAQ and NYSE Energy Sectors using Genetic Optimization<br />By Evan, L, Hjelmstad<br />University of Advancing Technology<br />ABSTRACT<br />With the currently unstable economy and the upcoming transfer of our dependency on oil to renewable and cleaner energy, it is often hard to create a portfolio of energy stocks with solid financials, especially if the investor is inexperienced. A program that utilizes genetic algorithms to analyze and compare stocks in the NASDAQ and NYSE energy sectors could possibly aid an investor in creating such a portfolio. It is the intent of this project to create such a program that utilizes a genetic algorithm to quickly and efficiently compile a portfolio that is the best possible for the energy sector. It will determine this by taking into account the fundamental analysis and dividends of its individual stocks and attempting to find a balance between them. <br />TABLE OF CONTENTS<br />ABOUT THE INNOVATION………………………………………InnovationToday’s Situation about the InnovationInnovation Timeline Innovation InquiryREVIEW OF RELATED MATERIALS……………………………..LEARNING PROCESS………………………………………………RESULTS…………………………………………………………….REFERENCES……………………………………………………….APPENDIX A. Portfolio Analysis Results Excel Insert……………...APPENDIX B. Adjusted Scoring Ranges…..………………………...223456111822---25<br />ABOUT THE INNOVATION<br />Innovation <br />Due to the unstable US economy, reduction of dependence on foreign oil and transferring that dependence to renewable energy, it is more difficult than ever for an individual to make a balanced portfolio of energy stocks. A computer program that utilizes genetic algorithms can help to aid an individual investor’s decision by giving unbiased stock picks that are based on solid financials. The purpose of this project is to see if the use of a genetic algorithm to determine and sort through the most financially healthy energy stocks and then compiling these into a portfolio will be more profitable than a randomly compiled energy portfolio. For the scope of this project it will only look at the NASDAQ and NYSE, specifically their energy sectors. However, if using a genetic algorithm can assist an individual on the NASDAQ and NYSE energy sectors it is reasonable to assume that it would work for the energy sectors of other exchanges with minimal changes. <br />This project will be done in several stages. The first stage will select the most secure small, medium and large cap stocks off the NASDAQ and NYSE energy indexes using the fundamental analysis methods explained later. The next stage will generate a random population of portfolios using the previously selected small, medium and large cap stocks as its population. The last stage will use a genetic algorithm which would recombine and evolve the population of portfolios until it found the final portfolio that best fit the criteria.<br />Today’s Situation about the Innovation <br />The NYSE was created in 1792 and remains one of the most respected stock exchanges in the world. However, it also remains a purely broker to broker floor traded system where individual investors cannot execute trades themselves. When the NASDAQ was established in 1971 it was the first implementation of electronic stock trading and ever since then it has been easy for individuals to manage their own investments. One thing that individual investors learn early on is how easy it is to get emotionally involved while trading stocks. Remaining emotionless during trades is a critical aspect of investing. (Paulos, 2003). This is where automated investment and analysis programs come into play.<br />Automated investment and analysis computer programs have existed since the 1980’s but until recently were only used by large banks and investment companies as these were the only entities that could afford to have such programs made. (Khan & Sharma, 2008). Recently however, the amount of individual investors has increased, thus increasing the appeal for the mass market of investor aiding programs. These programs analyze stocks from a purely statistical and analytical point of view, only reacting to changes in data. They are not susceptible to outside influences such as emotions, rumors or stress. According to The Korea Times, “A third of all EU and U.S. stock trades in 2006 were driven by such automatic programs, or algorithms, and the figure will reach 50 percent by 2010, according to Boston-based consulting firm Aite Group.” (Jin-seo, 2008, koreatimes.co.kr). Taking this into account, it is easy to see how the market for programs that aid investor decisions will only increase over the years. <br />Innovation Timeline <br />This project is broken into several key sections. The first is acquiring the skills necessary to complete the project as well as doing the initial research and designs. The second is creating the program by implementing the designs. Finally the third and last section is testing the genetic portfolio against a multitude of random portfolios and then analyzing the results.<br />The following is a week by week breakdown of what will be accomplished. <br />Weeks 1 – 12Learn basics of C#, experiment with Genetic Algorithms<br />Weeks 12 – 15Design graphical user interface<br />Weeks 15 – 18Code program to gather needed information off of the web<br />Weeks 18 – 20Code program to take user input<br />Weeks 20 – 26Code Genetic Algorithm<br />Weeks 26 – 30Test, debug and determine most efficient criteria for genetic algorithm <br />Week 30Run program to create genetic portfolio<br />Week 30Compile random portfolios<br />Weeks 30 – 34Track portfolios progress<br />Weeks 34 – 40Analyze and document results<br />Innovation Inquiry <br />The purpose of this project is to see if the use of a genetic algorithm to determine and sort through the most financially healthy energy stocks and then compiling these into a portfolio will be more profitable than a randomly compiled energy portfolio. For the scope of this project it will only look at the NASDAQ and NYSE, specifically their energy sectors. At the conclusion of this project, through the analysis methods described below, it will be determined if using a genetic algorithm is a viable solution to aiding investors in compiling a balanced and solid portfolio of stocks. This will be determined during the analysis stage of this project and will involve competing one genetic energy portfolio that the program generates against 100 randomly compiled energy portfolios. The analysis will take place over four stages and if the genetic portfolio outperforms over half of the random portfolios in over half of the time periods it will be considered successful. The time periods will be as follows, one week, two weeks, one month, and one year (using 11 months of prior data if available).<br />REVIEW OF RELATED MATERIAL<br />Introduction<br />The idea of being able to outsmart the stock market or create a strategy that predicts which direction it will move has been the fantasy of professional and private investors alike. This is shown through the creation of such fictional movies like Pi (1998) and many books such as The Five Greatest Stock Traders of All Time that depict stories of people that do manage to outsmart the market for at least short while and the strategies they used. Regardless of the success that a strategy may meet, the drive for more efficient strategies that use increasingly complex techniques will never stop.<br />Techniques to Analyze a Stock<br />In the beginning the techniques used to analyze a stock were simple. Many times investors would look at the fundamentals of a company such as what industry they were in, what direction they were taking, who ran the company and what their near term goals were. Then the techniques grew more complicated as the stocks began to be analyzed quantitatively and numerous ratios were used to judge the safety and value of a stock. Collectively these techniques are known as fundamental analysis. After fundamental analysis came technical analysis which involves using a series of techniques to analyze a company’s stock chart. From this an investor can attempt to determine a variety of things including which direction the stock will move. (Motley Fool Staff, 2008). <br />Genetic Algorithms<br />While the above methods help an investor analyze a stock, it does not provide any conclusive evidence as to what separates a successful stock from an unsuccessful one. With ever increasing amounts of processing power and more advanced algorithms being produced and improved every day, the idea of being able rationalize the movement of certain stocks to specific variables and causes is becoming more of a reality. One way that this is being achieved is through the use of genetic algorithms. <br />Genetic Algorithms are a subset of evolutionary algorithms that are based on inheritance, mutation, selection and crossover. They work by essentially sorting through a population using a variety of parameters until they have a new population. Just as the theory of evolution works in real life, in each generation the most fit genes are kept and the rest are thrown out. (Shapcott, 1992). They then repeat this process narrowing down the results more and more each time until they have the correct or most correct answer. This can be helpful in creating an efficient and balanced portfolio by sorting through a selection of stocks until it has the very best (defined by its parameters) of each industry or sector. These can then be compiled into a balanced portfolio. Each stock is assigned a score based on different criteria and then randomly assigned to a portfolio. This is then repeated until a specified number of random portfolios are created. The genetic algorithm would then proceed through a specified number of generations, keeping the most fit portfolios and randomly mutating these with a selected list of stocks until a prime portfolio was discovered. (Thatcher, 2004; Orito & Yamazaki, 2001). <br />Other Variations involving Genetic Algorithms<br />From the base idea of using genetic algorithms to create a portfolio of stocks, many variations have been created. One idea consists of implementing a neural network into the genetic optimization. Neural networks are closely related to genetic algorithms in that they use a biological process to analyze and sort data to solve a specific problem. Neural networks are structured similar to a human brain in that they are made up of neurons which essentially implement the basic structure of how the human brain makes connections. Thus a web of connections is created throughout a data source that can be explored for a solution. By implementing this functionality into a genetic algorithm it would allow the program to actually learn as it creates portfolios and theoretically improve its methods each time it executes. This offers additional ideas for expanding the project upon its completion. (Khan & Sharma, 2008).<br />Another variation improves upon the initial idea by using two separate genetic algorithms in a two step process to create the portfolio. The first step consists of selecting only the most fit stocks to begin with and compiling these into a list. This list is then used in the creation of the random portfolios. This is more efficient as any particularly weak stocks have already been eliminated. Essentially, the random portfolios consist of only the best, resulting in the final portfolio theoretically containing the best of the best. It is an adaptation of this method which I plan to employ to analyze the energy sector of the NASDAQ and NYSE, but rather than using a two stage genetic algorithm, I will be using a more direct analysis and sorting algorithm before I deploy the genetic algorithm. (Keung, Yu, Wang, & Zhou, 2006). <br />In addition to genetic algorithms being utilized to compile portfolio of stocks, they have been used with much success in comparing and contrasting trading strategies to determine the best combination of strategies and the optimum situations in which to deploy them. This was accomplished by using the behavior of groups of stock traders that was determined by variables such as amount of daily trades, the volume of these trades, current price of the specific stocks and whether they took a profit or loss on these trades and then using this information as the data set. The population for the algorithm was compiled of objects that represented these traders via rule sets which live stock data was fed into each day. The rule sets determined if that specific trader bought or sold a specific stock and recorded if they made a profit. What was discovered was that the rule sets that were output by this algorithm far exceeded any individual rule sets profitability. While it is not the purpose of this project to create such an algorithm it does offer many ideas for expanding this project upon its successful completion. (Eiben and Smith 2007).<br />Problems Faced<br />One problem faced with the ever increasing complexity of algorithms in the investment arena is that as one algorithm reaches the efficiency of another algorithm the competition between the two decreases their profits. Thus the developers for the algorithms are stuck in a never ending battle for creativity, complexity and efficiency. (Duhigg, 2006). <br />Another problem that is often faced is the dataset for the genetic algorithm is incomplete. The algorithm relies entirely on the data for generating great results and without a complete dataset, which is made difficult by the massive amount of companies that are listed on various exchanges, it cannot give optimum output.<br />Conclusion<br />Based on the research done on the application of genetic algorithms in regards to stock portfolio selection, an opportunity arises to see if this method can be successfully applied to a new and especially volatile sector such as that of the NASDAQ and NYSE energy sectors. If this is proven to be true then more research is merited to find out in what other volatile sectors genetic algorithms may prove useful and in what other ways they can be applied to stock market analysis.<br />LEARNING PROCESS <br />In order to prove that genetic optimization can indeed create a more profitable portfolio of energy stocks than one that is randomly selected, the program must be designed, created and tested. The program will be created in the C# programming language and will contain the following characteristics. <br />Upon running the user will be required to input the number of stocks desired in the portfolio. <br />The overall portfolio score will be out of 300 and will be comprised of several individual scores including how close the portfolio is to the dividend and fundamental analysis. <br />The user will not input a desired fundamental analysis score as it will always be at a desired score of 100. The fundamental analysis score of an entire portfolio will be the average of all the fundamental analysis scores of its individual stocks. <br />The user will also not be required to input a dividend score as the best possible balance between dividend and fundamental analysis will be sought. The default dividend will be at $1.00 per share but the total dividends can exceed this.<br />If an internet connection is available, it will navigate to NASDAQ.com and will read information about all of the companies that are listed under the energy sector. This information includes stocks on both the NASDAQ and NYSE. If an internet connection is not available, it will use the most recent data contained in a database. From this it will assign points to each company based on the fundamental analysis of its stock and will determine if the stock is a small, medium or large cap stock. For each of these categories the most financially secure stocks will be selected based on their fundamental analysis score. The collection of stocks that make up all three portfolios will make up a new population. From this new population the genetic algorithm will randomly create a population of portfolios which will be slightly and randomly adjusted (mutated) each generation until a portfolio is created that meets all of the user’s criteria. <br />Fundamental analysis involves an analysis of a company’s fundamentals such as its true value, assets owned, growth over the past quarters and debt status. The fundamental analysis score will be comprised of ten sub scores which will add up to its total score of 100. This score will then be doubled to be out of 200 to give fundamental analysis double the weight of the dividend score which will be out of 100. Together, the fundamental analysis and dividend scores will be out of 300 which will provide direct comparisons between portfolios. The individual methods of analysis which are described below were chosen as they represent some of the most common methods for determining the health of a stock. If the stock receives great results across all of the categories it is a huge indicator that this stock is likely to do better in the long term than other stocks with less impressive results. Please note however that while the scales of each method are described as being between 1 and 10 there are exceptions where they can receive a negative score for abnormally bad results or extra points for abnormally great results in a specific category, with the max score at either end being -5 and 15. The 1 through 10 score is used to total to 100 and most scores fall within this range. The exact process was determined through a series of trials which can be found in Appendix B.<br />Trailing P/E – The trailing P/E of a stock will be scored on a scale of 1 – 10. The price to earnings of a company is often considered the king of fundamental analysis measures. Since this is the trailing price to earnings it compares the current price of a stock with its past earnings. A P/E ratio gives the buyer an idea of how much earnings power they are buying and is a standard way to compare two companies’ earnings that have different stock prices. However, more than the P/E ratio of a company must be taken into account, as in the past companies have been able to manipulate their earnings in such a way that they appear much more solvent than they really are. Two historical examples of this are Enron and WorldCom. More recent examples include Lehman Brothers and Merrill Lynch. (Kelly, 2003).<br />P/B – The P/B ratio of a stock will be scored on a scale of 1 – 10. The price to book ratio compares a stock’s price with the total value of the company. This helps an investor judge if the stock they are buying is under or overvalued. If price to book ratio is one then the stock is selling for exactly what it is worth. If it sells for more than it is worth than its ratio will be above one. However, being above one is not always a concern. Some companies have intrinsic value that is not reflected in their book value. (Kelly, 2003).<br />P/S - The P/S ratio of a stock will be scored on a scale of 1-10. The price to sales value of a stock compares its stock price with its total sales for the last four quarters. It is advantageous to include a price to sales ratio in your analysis as while companies can manipulate earnings to their liking, it is practically impossible to manipulate your sales. (Kelly, 2003). <br />Current Ratio – The current ratio of a company will be scored on a scale of 1 – 10. The current ratio gives you an idea of how solvent a company is. It is simply an assets divided by liabilities ratio. Ideally you would like to see a company have at least a 2 to 1 current ratio. (Kelly, 2003). <br /> <br />Quick Ratio –The quick ratio of a company will be scored on a scale of 1 – 10. The quick ratio is similar to the current ratio but gives a more accurate reading of how well a company can deal with unseen expenses or opportunities as it is only the cash on hand divided by the current liabilities. Ideally you would like to see a quick ratio of at least .5. (Kelly, 2003). <br /> <br />Net Profit Margin – The net profit margin of a company will be scored on a scale of 1 – 10. The net profit margin of a company is found by dividing the money it has left after paying expenses by the money it had before paying expenses. This gives you an idea of how much money a company keeps from its revenue. This is also a great way to compare companies within the same industry. If two companies are of similar size, create similar products, have similar stock prices and other similar fundamental analysis scores it may be difficult to tell which company is healthiest. However, this will be clear when you look at the net profit margin as the company with the higher margin has found how to squeeze more profit out of their sales. This means if business becomes challenging they will be more likely to survive and adapt. (Kelly, 2003).<br />Cash Flow per Share – The cash flow per share will be scored on a scale of 1 – 10. The cash flow per share is simply the company’s total cash flow divided by the total number of shares. A company’s cash flow tells you how well that company manages its money and how efficiently it reinvests it. By dividing it by the total number of shares you can see how much you must pay for a share of the company’s cash flow. (Kelly, 2003). <br />Beta – The beta will be scored on a scale of 1 – 10. The beta of a company compares how volatile its stock is with the rest of the market. The NASDAQ is measured by the NASDAQ 500 which is comprised of the five hundred most influential stocks on the exchange. The NASDAQ 500 has a beta of one. Every single stocks beta in the NASDAQ is in relationship to the NASDAQ 500’s beta. The NYSE is measured by the S&P 500 which again is comprised of the five hundred most influential stocks on the exchange. The S&P 500 also has a beta of one, thus no changes need to be made when analyzing the beta of a stock on either exchange. If a stock has a beta higher than one it can either mean that the stock has been more successful than the market, or it has not been as successful. If its beta is less than one that could mean that the market has been successful but the stock has generally stayed the same. It could also mean that the market is doing worse and that stock is not going down but again staying the same. Either way a good solid stock should have a beta slightly greater than one. (Kelly, 2003).<br />ROE – The ROE of a stock will be scored on a scale of 1 – 10. The return on equity of a company is what many people believe to be the greatest measure of a stock’s success. This tells how the stock has done in the past by dividing the net income by the total shareholders’ equity. This is essentially determining how much money a company has made from the investments it made with shareholders money. (Kelly, 2003). <br />EPS – The EPS of a company will be scored on a scale of 1 – 10. The earnings per share of a company is a standard way to measure the growth of a company. However, like with the P/E ratio it can be easily manipulated. The earnings per share is simply the total earnings for that quarter or year divided by the total amount of shares outstanding. Ideally you want this number to be as large as possible. (Kelly, 2003).<br />A dividend is a way that a company shares profit with its shareholders and is a specific amount that is paid each quarter for each share of stock an investor owns of that company. Many people wish to incorporate dividend paying stocks into their portfolio as it offers a guaranteed income even if the current markets are volatile. The target amount is $1.00 of dividends per share. It is assumed that it is alright if the portfolio exceeds that amount of dividends but it is not alright if it falls short and thus for every one cent that the total dividend amount falls short of the requested amount, one point will be subtracted from the total score of 100. The portfolio dividend score will be an average of all the dividends of its stock resulting in the dividends per share of that portfolio. <br />Upon completion of the program running, the test phase will begin and the price of each stock will be noted. For the sake of testing, an even amount of virtual money will be invested in each stock. Then, another program will randomly create fifty portfolios of the same number and ratio of small, medium and large cap stocks but drawing from the entire energy sector rather than the genetically optimized one. In addition, five people will each be asked to create ten random portfolios again based on the entire energy sector but again keeping the same ratio of small, medium and large cap stocks in each one. This will ensure that neither one method is biased in its randomization and will result in exactly one hundred randomly created portfolios and one genetically created portfolio. Again, the price of each stock will be noted and an even amount of virtual money invested. Due to the constant fluctuation of the market, the portfolios will be compared and ranked after one week, two weeks, one month, and one year (using prior data if available). If the genetically created portfolio has outperformed a majority of the randomly created portfolios in at least two of these time frames then the project will be deemed successful. If not, an attempt will be made to isolate the variables that led to it being outperformed for future correction. <br />RESULTS<br />Thus far the project has turned out better than expected as it exceeded each of the criteria’s for success by outperforming a majority of the random portfolios in three of the time slots and tying in one. Development of the program was completed in late May but many additional features have been added to it since. On June 8th, 2009, the analysis stage began which involved competing the genetic portfolio the program created against 100 random portfolios. As stated earlier, the analysis was performed in four stages across different time periods: one week, two weeks, one month and using 11 months of prior data (if available), one year. The following is a breakdown of how the genetic portfolio did during each stage and how it compared to the random portfolios. While the random portfolios exact percentage gains and losses are too numerous to state in this report, you can view all the data gathered during the analysis stage in Appendix A.<br />Analysis Period One (One Week)<br />2.35% overall genetic portfolio gain<br />50 random portfolios outperformed / 50 random portfolios outperformed by<br />Analysis Period Two (Two Weeks)<br />-5.55% overall genetic portfolio loss<br />93 random portfolios outperformed / 7 random portfolios outperformed by<br />Analysis Period Three (One Month)<br />-9.51% overall genetic portfolio loss<br />74 random portfolios outperformed / 26 random portfolios outperformed by<br />Analysis Period Four (One Year, using 11 Months of Prior Data)<br />-43.85% overall genetic portfolio loss<br />71 random portfolios outperformed / 28 random portfolios outperformed by / 1 tied<br />Although at first glance it may seem as if the algorithm was unsuccessful as there was only one time period when the portfolio was profitable, the goal of this project was to create a program that would consistently outperform a random portfolio and thus aid an investor in being profitable. Anyone who has invested in the stock market before understands that you cannot always be profitable as there are times when all stocks go down. During times like these it is your goal to lose as little money as possible which is exactly what the genetic portfolio accomplished. Even while the genetic portfolio lost value, when compared to the random portfolios it was doing extremely well. In fact, the genetic portfolio only started excelling against the random portfolios when the energy sectors of the NASDAQ and NYSE took a turn for the worse. This indicates that this portfolio is not nearly as volatile and is much more financially stable than the random portfolios. During the first analysis period there were exactly fifty portfolios that outperformed the genetic portfolio. However, these portfolios quickly lost their value as soon as bad news hit the sector, whereas the genetic portfolio was not hit nearly as hard. During the next two analysis periods the genetic portfolio outperformed an incredible 93 and 74 random portfolios respectively. Even when using 11 months of prior data combined with the one month of current data during the fourth analysis stage, the genetic portfolio outperformed an amazing 71 random portfolios. Although the portfolio posted a loss during this time period of 43.85%, this is due to the horrendous market conditions caused by the failures of many prominent banks and auto companies during the last months of 2008. It is believed that it will be several years before the markets fully recover from such a hard hit. The data stated above is more easily seen in the graph below. <br />Thus this project can be deemed successful by the criteria stated earlier which stated that the genetic portfolio must outperform at least half of the random portfolios in at least half of the analysis periods. The genetic portfolio beat a majority of the portfolios in three of the time periods and tied in one.<br />As it has been proven that the current fundamental analysis methods when combined with a genetic algorithm can produce an above average portfolio in the energy sector, there is still much to be explored. These same techniques can be applied to other sectors and even exchanges as well. Furthermore, the fundamental analysis methods can be used separate from the genetic algorithm to rank and display the top stocks from each sector and exchange, further helping an investor make educated decisions. Regardless of the direction this project runs, the first successful stepping stone is in place. <br />REFERENCES<br />Eiben, A. E., Smith, J. E. (2007). Introduction to Evolutionary Computing. New York: Springer Berlin Heidelberg.<br />Jin-seo, C. (06/29/2008). The Korea Times: Algorithm Stock Trading Spreading. Retrieved September 24th, 2008, from http://www.koreatimes.co.kr/www/news/biz/2008/06/123_26677.html. <br />Kelly, J. (2003). The Neatest Little Guide to Stock Market Investing. New York: Plume.<br />Keung Lai, K., Yu, L., Wang, S., Zhou, Z. A Double Stage Genetic Optimization Algorithm for Portfolio Selection. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.9105. <br />Khan, A. & Bandopadhyaya, T. K., & Sharma, S. (2008). Comparisons of Stock Rates Prediction Accuracy using Different Technical Indicators with Back propagation Neural Networks and Genetic Algorithm Based Back propagation Neural Networks. Proceedings of the First International Conference on Emerging Trends in Engineering and Technology. Retrieved from http://ieeexplore.ieee.org/servlet/opac?punumber=4579839.<br />Lin, L., & Cao, L., & Wang, J., & Zhang, C. (2000). The Applications of Genetic Algorithms in Stock Market Data Mining. Retrieved from http://www-staff.it.uts.edu.au/~lbcao/publication/DM2004.pdf.<br />Motley Fool Staff. (2008). Investing Strategies: Your First Stock. Retrieved October 11th, 2008 from http://www.fool.com/investing/beginning/investing-strategies-your-first-stock.aspx.<br />Orito, Y. & Yamazaki, G. (2001). Index Fund Portfolio Selection Using GA. Proceedings of the Fourth International Conference on Computational Intelligence and Multimedia Applications. Retrieved from http://ieeexplore.ieee.org/servlet/opac?punumber=7656.<br />Paulos, J. (2003). A Mathematician Plays the Stock Market. New York: Basic Books.<br />Shapcott, J. Index Tracking: Genetic Algorithms for Investment Portfolio Selection. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.7737.<br />Thatcher, G. (2004). Using Genetic Algorithms to Analyze Stock Portfolios. Retrieved September 24th, 2008, from http://www.gregthatcher.com/Papers/AI/GeneticAlgorithms.aspx.<br />Wang, J. (1998). Evolutionary Stock Trading Decision Support System Using Sliding Window. Proceedings of the 1998 International Conference on Evolutionary Computation. Retrieved from http://ieeexplore.ieee.org/servlet/opac?punumber=5621.<br />Wang, Y. & Tang, W. (2005). Research on Intelligent Algorithm for Portfolio Selection with Credibility Criterion. Proceedings of the Fourth International on Machine Learning and Cybernetics. Retrieved from http://ieeexplore.ieee.org/servlet/opac?punumber=10231.<br />APPENDIX B. Adjusted Scoring Ranges <br />Upon completion of the initial rating and sorting system a series of test were begun to optimize and tweak this process before feeding the resulting data into the genetic algorithm. When the top companies were looked over by hand it was discovered that the previously determined rating system was inefficient at properly sorting great companies out from the phenomenal. As the energy sector tends to be abnormally volatile when compared to other sectors and as small cap stocks are generally more volatile than medium and large cap stocks, small cap stocks were used as an indicator of how balanced the rating system was. Below are the top five companies from the small cap subcategory and the corresponding data for each company.<br />1: 91.847GulfMark Offshore, Inc.<br />2: 90.261EV Energy Partners, L.P.<br />3: 87.664Contango Oil & Gas Company<br />4: 86.793Goodrich Petroleum Corporation<br />5: 84.799Clayton Williams Energy, Inc.<br />1: GulfMark Offshore, Inc.<br />Total FA: 91.847<br />Score: 1.43Trailing P/E: 3.43<br />Score: 10P/B: 0.73<br />Score: 1.54P/S: 1.54<br />Score: 8.843Current Ratio: 8.843<br />Score: 10Quick Ratio: 2<br />Score: 12Net Profit Margin: 44.64<br />Score: 9.97Cash Flow Per Share: 3.97<br />Score: 10Beta: 1.25<br />Score: 9.604ROE: 24.01<br />Score: 10EPS: 7.56<br />2: EV Energy Partners, L.P.<br />Total FA: 90.261<br />Score: 0Trailing P/E: 1.5<br />Score: 10P/B: 0.59<br />Score: 1.31P/S: 1.31<br />Score: 10Current Ratio: 10<br />Score: 10Quick Ratio: 2<br />Score: 12Net Profit Margin: 109.61<br />Score: 8.581Cash Flow Per Share: 2.581<br />Score: 7.68Beta: 0.96<br />Score: 12ROE: 60.9<br />Score: 10EPS: 11.14<br />3: Contango Oil & Gas Company<br />Total FA: 87.664<br />Score: 1.74Trailing P/E: 3.74<br />Score: 10P/B: 1.84<br />Score: 3.17P/S: 3.17<br />Score: 5.024Current Ratio: 5.024<br />Score: 9.4Quick Ratio: 1.2<br />Score: 12Net Profit Margin: 90.34<br />Score: 10Cash Flow Per Share: 4.722<br />Score: 9.84Beta: 1.27<br />Score: 12ROE: 42.13<br />Score: 10EPS: 11<br />4: Goodrich Petroleum Corporation<br />Total FA: 86.793<br />Score: 4.31Trailing P/E: 6.31<br />Score: 10P/B: 1.18<br />Score: 3.58P/S: 3.58<br />Score: 7.708Current Ratio: 7.708<br />Score: 9.6Quick Ratio: 1.3<br />Score: 12Net Profit Margin: 63.06<br />Score: 9.921Cash Flow Per Share: 3.921<br />Score: 6.08Beta: 0.76<br />Score: 10.854ROE: 29.27<br />Score: 9.48EPS: 3.48<br />5: Clayton Williams Energy, Inc.<br />Total FA: 84.799<br />Score: 0.76Trailing P/E: 2.76<br />Score: 10P/B: 1.19<br />Score: 0.72P/S: 0.72<br />Score: 4.084Current Ratio: 4.084<br />Score: 8Quick Ratio: 0.5<br />Score: 12Net Profit Margin: 26.97<br />Score: 9.395Cash Flow Per Share: 3.395<br />Score: 8.56Beta: 1.43<br />Score: 12ROE: 59.11<br />Score: 10EPS: 11.67<br />The next trial used a completely unrestricted rating system where there were no minimum or maximum caps. While this did increase the gaps between the stocks, it resulted in unrealistic differences that would only serve to skew the output of the genetic algorithm. Below are the top five small cap stocks and the corresponding data for each company.<br />1: 3952.39North European Oil Royality Trust<br />2: 144.18New Concept Energy, Inc<br />3: 122.73Permian Basin Royalty Trust<br />4: 102.986EV Energy Partners, L.P.<br />5: 99.373Contango Oil & Gas Company<br />1: North European Oil Royality Trust<br />Total FA: 3952.39<br />Score: 4.96Trailing P/E: 6.96<br />Score: 10P/B: 1<br />Score: 6.66P/S: 6.66<br />Score: 4.068Current Ratio: 4.068<br />Score: 0Quick Ratio: 0<br />Score: 18.213Net Profit Margin: 97.13<br />Score: 4.312Cash Flow Per Share: 1.078<br />Score: 1.84Beta: 0.23<br />Score: 3898.342ROE: 38908.42<br />Score: 9.975EPS: 3.975<br />2: New Concept Energy, Inc<br />Total FA: 144.18<br />Score: -1.65Trailing P/E: 0.35<br />Score: 10P/B: 0.34<br />Score: 2.31P/S: 2.31<br />Score: 8.34Current Ratio: 8.34<br />Score: 8.6Quick Ratio: 3.5<br />Score: 61.272Net Profit Margin: 527.72<br />Score: 1.4Cash Flow Per Share: 0.35<br />Score: 8.64Beta: 1.42<br />Score: 21.94ROE: 144.4<br />Score: 16.258EPS: 10.258<br />3: Permian Basin Royalty Trust<br />Total FA: 122.73<br />Score: 2.41Trailing P/E: 4.41<br />Score: -832.4P/B: 423.2<br />Score: 4.39P/S: 4.39<br />Score: 4Current Ratio: 4<br />Score: 0Quick Ratio: 0<br />Score: 18.421Net Profit Margin: 99.21<br />Score: 0.44Cash Flow Per Share: 0.11<br />Score: 4.32Beta: 0.54<br />Score: 911.928ROE: 9044.28<br />Score: 8.391EPS: 2.391<br />4: EV Energy Partners, L.P.<br />Total FA: 102.986<br />Score: -0.5Trailing P/E: 1.5<br />Score: 10P/B: 0.59<br />Score: 1.31P/S: 1.31<br />Score: 8.734Current Ratio: 8.734<br />Score: 8.3Quick Ratio: 2<br />Score: 19.461Net Profit Margin: 109.61<br />Score: 8.581Cash Flow Per Share: 2.581<br />Score: 7.68Beta: 0.96<br />Score: 13.59ROE: 60.9<br />Score: 17.14EPS: 11.14<br />5: Contango Oil & Gas Company<br />Total FA: 99.373<br />Score: 1.74Trailing P/E: 3.74<br />Score: 10P/B: 1.84<br />Score: 3.17P/S: 3.17<br />Score: 5.024Current Ratio: 5.024<br />Score: 8.14Quick Ratio: 1.2<br />Score: 17.534Net Profit Margin: 90.34<br />Score: 10.722Cash Flow Per Share: 4.722<br />Score: 9.84Beta: 1.27<br />Score: 11.713ROE: 42.13<br />Score: 17EPS: 11<br />The next and final trial involved using an adjusted rating system similar to the one above but with minimum and maximum restrictions. After a company’s financials surpass the 1 through 10 rating scale on either end, a new scale kicks in which adds points to the rating scale much less willingly and subtracts points slightly less willingly. This properly allowed the exceeding companies to stand out and helped to separate the companies that had abnormally bad ratings but were not distinguished before as their financials capped the rating scale. Overall, this creates a rating scale where it is much easier to lose points then it is to gain them, again aiding in selecting only the most financially secure stocks. Below are the top five small cap stocks and the corresponding data for each company.<br />1: 96.385EV Energy Partners, L.P.<br />2: 94.839Contango Oil & Gas Company<br />3: 93.997GulfMark Offshore, Inc.<br />4: 90.407Clayton Williams Energy, Inc.<br />5: 89.71New Concept Energy, Inc<br />1: EV Energy Partners, L.P.<br />Total FA: 96.385<br />Score: -0.5Trailing P/E: 1.5<br />Score: 10P/B: 0.59<br />Score: 1.31P/S: 1.31<br />Score: 8.734Current Ratio: 8.734<br />Score: 8.3Quick Ratio: 2<br />Score: 15Net Profit Margin: 109.61<br />Score: 8.581Cash Flow Per Share: 2.581<br />Score: 7.68Beta: 0.96<br />Score: 13.59ROE: 60.9<br />Score: 15EPS: 11.14<br />2: Contango Oil & Gas Company<br />Total FA: 94.839<br />Score: 1.74Trailing P/E: 3.74<br />Score: 10P/B: 1.84<br />Score: 3.17P/S: 3.17<br />Score: 5.024Current Ratio: 5.024<br />Score: 8.14Quick Ratio: 1.2<br />Score: 15Net Profit Margin: 90.34<br />Score: 10.722Cash Flow Per Share: 4.722<br />Score: 9.84Beta: 1.27<br />Score: 11.713ROE: 42.13<br />Score: 15EPS: 11<br />3: GulfMark Offshore, Inc.<br />Total FA: 93.997<br />Score: 1.43Trailing P/E: 3.43<br />Score: 10P/B: 0.73<br />Score: 1.54P/S: 1.54<br />Score: 8.169Current Ratio: 8.169<br />Score: 8.3Quick Ratio: 2<br />Score: 12.964Net Profit Margin: 44.64<br />Score: 9.97Cash Flow Per Share: 3.97<br />Score: 10Beta: 1.25<br />Score: 9.604ROE: 24.01<br />Score: 13.56EPS: 7.56<br />4: Clayton Williams Energy, Inc.<br />Total FA: 90.407<br />Score: 0.76Trailing P/E: 2.76<br />Score: 10P/B: 1.19<br />Score: 0.72P/S: 0.72<br />Score: 4.084Current Ratio: 4.084<br />Score: 8Quick Ratio: 0.5<br />Score: 11.197Net Profit Margin: 26.97<br />Score: 9.395Cash Flow Per Share: 3.395<br />Score: 8.56Beta: 1.43<br />Score: 13.411ROE: 59.11<br />Score: 15EPS: 11.67<br />5: New Concept Energy, Inc<br />Total FA: 89.71<br />Score: -1.65Trailing P/E: 0.35<br />Score: 10P/B: 0.34<br />Score: 2.31P/S: 2.31<br />Score: 8.34Current Ratio: 8.34<br />Score: 8.6Quick Ratio: 3.5<br />Score: 15Net Profit Margin: 527.72<br />Score: 1.4Cash Flow Per Share: 0.35<br />Score: 8.64Beta: 1.42<br />Score: 15ROE: 144.4<br />Score: 15EPS: 10.258<br />

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